Agriculture Reference
In-Depth Information
The most common bacterial phyla identified by 16S gene sequences
(Proteobacteria, Actinobacteria, Cytophagales, Planctomycetes, Verrucomicrobia,
and the Acidobacteria) were also investigated by the characterization of RNA
extracted from KBS LTER soils. Because the concentration of rRNA in a cell is
positively correlated with growth rate, direct probing of RNA provides an estimate
of changes in the overall metabolic status of microbes. Changes in rRNA abundance
reveal that soil microbial communities are dynamic and capable of responding to
seasonal events. The relative abundance of microbial groups is also affected by local
environments, so recognizable patterns of community structure can be related to
land management (Buckley and Schmidt 2003). It is also worth noting that a low
ratio of rRNA to rRNA-encoding genes suggests low overall metabolic activity,
leading Jones and Lennon (2010) to propose that dormancy contributes to the main-
tenance of microbial diversity in lakes. Given the extensive diversity and low growth
rates in soil microbes, it is worth considering dormancy as a major mechanism for
the preservation of diversity in terrestrial habitats as well (Lennon and Jones 2011).
While detailed taxonomic characterization of communities can be derived by
targeting the 16S rRNA encoding genes, analysis of shotgun metagenomes is cur-
rently the best approach for identifying the metabolic potential of a community. This
provides a comprehensive catalog of DNA sequences in the soil and can indicate,
through similarity to known genes, the relative abundance of metabolic functions
and pathways that are encoded in that soil. Such data from KBS LTER revealed a
previously unknown and systematic artifact in metagenomes (Gomez-Alvarez et al.
2009) that can be identified and removed with an online tool (Teal and Schmidt
2010)—a critical step in making quantitative metagenome comparisons. With this
artifact removed, an initial assessment of the functional diversity in KBS LTER soils
was made from replicate plots of the MCSE Deciduous Forest and Conventional
corn-soybean-wheat systems. The metagenomes were annotated using the
MG-RAST tool developed at Argonne National Laboratories (
http://metagenomics.
nmpdr.org
) and compared to metagenomes from other biomes. Based on a princi-
pal components analysis of the annotated metagenomes, the functional diversity in
soils was clearly distinguished from other biomes (Fig. 6.2). Nitrogen metabolism
was one of the major features driving the distinction between the microbial com-
munities in soils from those in other environments.
Environmental Drivers of Diversity
Chemical and physical factors that affect the distribution of microbes in soil are
poorly understood. However, the application of molecular techniques is providing
the capacity to identify environmental factors that influence microbial distributions
in nature. Culture-independent approaches (Pace 2009) are particularly useful for
exploring the biology of bacteria from phyla that are poorly represented in culture
collections. These include one of the most abundant phyla in soil, the Acidobacteria.
In two recent studies (Eichorst et al. 2007, 2011), the distributions of Acidobacteria
in relation to physical and chemical characteristics of soil were determined across
the MCSE using partial sequencing of cloned 16S rRNA genes. The percentage of
subdivision 1 Acidobacteria was correlated with soil pH, being highest in the most
